Data Anomaly Detection

2. The system of claim 1, wherein the processor further executes the computer-executable instructions to iteratively train an anomaly detection algorithm from the plurality of the anomaly detection algorithms based at least in part on the user feedback.

3. The system of claim 1, wherein the processor further executes the computer-executable instructions to collect user behavior data for an anomaly detection algorithm from the plurality of the anomaly detection algorithms, wherein the anomaly detection algorithm is automatically recommended based on the collected user behavior data.

4. The system of claim 1, wherein the processor further executes the computer-executable instructions to display a slider bar as a user input sensitivity element and select an algorithm of the plurality of the anomaly detection algorithms to tune in a simulation, wherein a result of the simulation corresponding to a different sensitivity setting received at the slider bar is displayed to visualize the detected anomaly.

5. The system of claim 4, wherein the slider bar is displayed as a user interface (UI) element that allows an adjustment to a sensitivity.

6. The system of claim 1, wherein the processor further executes the computer-executable instructions to update the computer-executable instructions associated with the selected anomaly detection algorithm.

7. The system of claim 6, wherein the computer-executable instructions associated with the selected anomaly detection algorithm are updated using a crowdsourcing algorithm.

10. The computerized method of claim 9, further comprising collecting user behavior data for the updated anomaly detection algorithm, wherein the calibrated and updated anomaly detection algorithm is automatically recommended based on the collected user behavior data.

12. The computerized method of claim 8, wherein the selected anomaly detection algorithm is updated using a crowdsourcing algorithm.

13. The computerized method of claim 8, further comprising automatically adjusting a parameter of an adaptive Streaming Least Square (SLS) algorithm with an alert sensitivity setting received from a user, the parameter comprising one or more of the following: a lag time defined by a period of time for the user to confirm that data is abnormal, an SLS threshold defined by a counter value, and a zoom parameter defining a time granularity of a counter sample.

14. The computerized method of claim 13, wherein the adaptive SLS algorithm is configured to use a history of residual scores obtained from a sliding window to determine a predicted threshold from exponential smoothing of the history of residual scores, and applied to a current residual score, and in response to the current residual score exceeding the predicted threshold multiplied by a tolerance, an incoming data point on which the residual score is calculated is classified as an anomaly.

15. The computerized method of claim 8, further comprising receiving a user input, from a single slider element, to adjust an alert setting for the anomaly detection algorithm.

16. The computerized method of claim 15, further comprising performing sensitivity mapping to map a percentage variation from an upper bound and a lower bound of an alert threshold with a corresponding parameter setting to a sensitivity score based on a statistical analysis of data distribution of a counter to normalize an upper bound and a lower bound of sensitivity for different counters using a same anomaly detection algorithm based on the received user input from the single slider element.